Core Viewpoint - The article discusses the evolution and challenges of artificial intelligence (AI) in gaming and virtual environments, emphasizing the importance of interactive learning experiences over traditional pre-training methods. It critiques the limitations of large language models (LLMs) and highlights the need for more effective learning frameworks in AI development [16][18][19]. Group 1: Background and Development - Id Software, founded in the 1990s, played a significant role in the development of iconic games that contributed to GPU advancements and the modern AI landscape [3]. - The author has extensive experience in various tech companies, including Armadillo Aerospace and Oculus, focusing on the development of virtual reality technologies [6][8]. Group 2: Learning and AI Models - The article critiques the effectiveness of LLMs, arguing that many people do not fully understand their limitations, particularly in learning from new environments [16]. - It emphasizes the importance of interactive learning, suggesting that AI should learn through experiences similar to how humans and animals do, rather than relying solely on pre-trained models [16][18]. Group 3: Gaming and AI Interaction - The author notes that traditional gaming AI often relies on internal game structures, which can lead to cheating, while cloud gaming could mitigate this issue [18]. - The article discusses the limitations of current AI models in learning from games, highlighting that significant amounts of experience (e.g., 200 million frames) are required to reach human-level performance [20][34]. Group 4: Challenges in AI Learning - The article identifies ongoing challenges in continuous, efficient, and lifelong learning within AI, which are tasks that even simple animals can accomplish easily [20]. - It points out that many AI systems struggle with learning in complex environments, and traditional reinforcement learning frameworks may not be suitable for all scenarios [30][32]. Group 5: Future Directions - The author proposes a mixed approach to learning environments, combining passive and interactive content to enhance AI learning capabilities [22]. - The article suggests that new benchmarks should be established to evaluate AI performance across various games, focusing on long-term learning and retention of skills [95][97].